Word Alignment
84 papers with code • 7 benchmarks • 4 datasets
Word Alignment is the task of finding the correspondence between source and target words in a pair of sentences that are translations of each other.
Source: Neural Network-based Word Alignment through Score Aggregation
Most implemented papers
WSPAlign: Word Alignment Pre-training via Large-Scale Weakly Supervised Span Prediction
Most existing word alignment methods rely on manual alignment datasets or parallel corpora, which limits their usefulness.
Aligning and Prompting Everything All at Once for Universal Visual Perception
However, predominant paradigms, driven by casting instance-level tasks as an object-word alignment, bring heavy cross-modality interaction, which is not effective in prompting object detection and visual grounding.
Multilingual Distributed Representations without Word Alignment
Distributed representations of meaning are a natural way to encode covariance relationships between words and phrases in NLP.
Conditional Random Field Autoencoders for Unsupervised Structured Prediction
We introduce a framework for unsupervised learning of structured predictors with overlapping, global features.
Agreement-based Joint Training for Bidirectional Attention-based Neural Machine Translation
The attentional mechanism has proven to be effective in improving end-to-end neural machine translation.
Guided Alignment Training for Topic-Aware Neural Machine Translation
In this paper, we propose an effective way for biasing the attention mechanism of a sequence-to-sequence neural machine translation (NMT) model towards the well-studied statistical word alignment models.
A Web-Based Interactive Tool for Creating, Inspecting, Editing, and Publishing Etymological Datasets
The paper presents the Etymological DICtionary ediTOR (EDICTOR), a free, interactive, web-based tool designed to aid historical linguists in creating, editing, analysing, and publishing etymological datasets.